A Novel Operational Model for Interconnected Microgrids Participation in Transactive Energy Market: A Hybrid IGDT/Stochastic Approach

Daneshvar, Mohammadreza; Mohammadi‐Ivatloo, Behnam; Zare, Kazem; Asadi, Somayeh; Anvari‐Moghaddam, Amjad

doi:10.1109/tii.2020.3012446

Cited by 90 publications

(32 citation statements)

References 36 publications

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“…The results show almost 70% of cost reduction for electricity imported from local markets. The interaction of microgrids with 100% renewable power in the transactive energy markets is proposed in [34]. The case with local energy exchange brings 18.34 % cost reduction for each microgrid which highly motivates them for local energy sharing due to high energy prices for energy exchange with the main grid.…”

Section: B Literature Reviewmentioning

confidence: 99%

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Electricity Cost-Sharing in Energy Communities Under Dynamic Pricing and Uncertainty

et al. 2021

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Most of the prosumers nowadays are constrained to trade only with the supplier under a flat tariff or dynamic time-of-use price signals. This paper models and discusses the cost-saving benefits of flexible prosumers as members of energy communities who can exchange electricity among peers and on the wholesale markets through a community manager. Authors propose a novel centralized post-process sharing method by introducing a two-stage mechanism which, unlike the existing methods, guarantees benefits for prosumers joining the energy community. The first stage assesses internal price calculation in three different methods: Bill Sharing Method Net (BSMN), Mid-Market Rate Net (MMRN), and Supply-Demand Ratio Net (SDRN). In their original form, prices are calculated in a single stage and the comprehensive analyses in the paper show that some members face increased cost. To solve this issue, the paper improves the methods by introducing the second stage in which the compensation methodology is defined for the distribution of savings which ensures that all community members gain benefits. Results investigate the value of inner technical flexibility of the prosumer (flexible preferences of the final consumer can reduce the cost from 3% up to 20 %). Moreover, incentives/penalties encourage the utilization of a flexible behavior to adjust the real-time consumption of prosumers' appliances to a predefined day-ahead schedule. This type of pricing results in a lower amount of benefits sharing in the community (the reduction of 18-47% in MMRN and 49-114% in SDRN compared to existing pricing) which makes this incentives/penalties pricing more preferable. The paper concludes that prosumers with an excess PV production would not benefit from the internal energy exchange in the community under BSMN due to free energy exchange between members.INDEX TERMS cost-sharing, day-ahead market, demand response, energy community, peer-to-peer trading I. NOMENCLATUREStochastic and non-stochastic parameters are presented as bold text, while variables are a regular type of text. Where augmented with the subscripts s and t, they refer to the values they take on in scenario s and time period t, while the subscript d stands for a household and ap for a different uninterruptible flexible appliance. If not stated differently, variables and parameters are positive. Indices and Sets ap∈ AUninterruptible flexible appliances d∈ D Households d∈ D + Community members with decreased cost in the first stage d∈ D -Community members with increased cost in the first stage t ∈ T Time steps

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Section: B Literature Reviewmentioning

confidence: 99%

“…Only a few papers focus on this and model both the household level batteries and controllable smart home devices, such as [8], [19], [20], [24]. Other papers either model only the battery storage or focus more on MES aspects [28]- [34]. However, none of them considers post-processing cost-sharing methods in their analyses.…”

Section: B Literature Reviewmentioning

confidence: 99%

Electricity Cost-Sharing in Energy Communities Under Dynamic Pricing and Uncertainty

et al. 2021

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“…From a practical perspective, the exact distribution of uncertain variables is difficult to obtain for given empirical data as we later demonstrate in a case study for electricity prices and load profile, where machine learning-based approaches may deliver a better estimation for these uncertainties. To remove this drawback, the sampling technique can be adopted to transform the chance-constrained model to stochastic programming problems, where it provides an alternative way to handle uncertainties by generating discrete probabilistic scenarios of the uncertain parameter and integrating them into the optimization problems (see, e.g., Weron, 2005;Daneshvar et al, 2020b). Though this approach does not rely on specific types of distribution, both the solution quality and computational efficiency can be highly impacted by the scenarios that are utilized in the problem formulation.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Robust Scheduling for Networked Microgrids Under Uncertainty

Zhang¹,

Troitzsch²

2021

Front. Energy Res.

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This paper proposes a framework that facilitates the energy exchange between networked microgrids (MGs) in the electricity market. An alternating direction method of multipliers (ADMM)-based robust optimization algorithm is proposed to derive the optimal energy exchange strategy for the networked MGs considering the uncertainties of the electrical load, intermittent generation, and electricity prices in the external market. The proposed method naturally lends itself to a classical market-clearing problem between two hierarchical levels comprising (i) main-grid-to-MG and (ii) MG-to-MG, aiding in the result interpretation and practical realization. Leveraging from the decentralized organization, the operational autonomy and information privacy of each MG is protected. The proposed framework is tested on a modified 144-node network with 3 MGs. The numerical experiments demonstrate the convergence of the proposed market clearing to the market equilibrium in different grid operational scenarios with different conservatism parameter settings for MG operators.

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“…In power grids with a high portion of renewables, integration of the DERs is introduced as one of the effective manners to overcome the challenge of increasing the penetration of RERs across the power system 19 . In this regard, the integration of the microgrids as small scale power systems can be one of the promising ways to overcome the challenges ahead of the optimal scheduling of different types of DERs 20 .…”

Section: Introductionmentioning

confidence: 99%

Chance‐constrained scheduling of hybrid microgrids under transactive energy control

et al. 2021

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Summary The uncertainties related to renewable energy resources (RERs) and energy markets have a direct impact on the scheduling of hybrid microgrids and stand as a challenge in renewable‐based systems. Due to this, many mathematical methods are employed for modeling uncertainties, but applying a suitable approach is important for reaching accurate results. This paper presents the chance‐constrained programming technique to model the fluctuations in renewable outputs and electricity market prices to effectively deliberate the probabilistic nature of them. In this respect, the transactive energy concept is used to provide the energy sharing possibility for hybrid microgrids with a high portion of renewables for clean electricity generation. The interaction between the electrical and gas network as a result of using the gas‐fired devices in each microgrid, is also included in this study. To test the effectiveness of the suggested framework, the IEEE‐10 bus case study with five commercial hybrid microgrids is selected and the scheduling of microgrids is carried out in the interconnected electricity and gas networks. The different impacts of the proposed method are analyzed by considering two cases: optimal scheduling of hybrid microgrids without uncertainty modeling (Case I) and with it (Case II). In Case I and II, numerical results indicated $22 378.067 expected operation cost for Case II in comparison with $26 014.359 for Case I, which proves the effectiveness of the proposed model in probabilistic modeling of the system as well as achieving the economic benefits for hybrid microgrids. Highlights Optimal chance‐constrained DA scheduling of hybrid microgrids is effectively conducted. The CCP method is used for probabilistic evaluation of the problem in the presence of RERs. Transactive energy technology is employed for managing energy trading in the system. LHS and FFS methods are applied for scenario generation and reduction, respectively. The interactions between electricity and gas networks are effectively modeled.

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A Novel Operational Model for Interconnected Microgrids Participation in Transactive Energy Market: A Hybrid IGDT/Stochastic Approach

Cited by 90 publications

References 36 publications

Electricity Cost-Sharing in Energy Communities Under Dynamic Pricing and Uncertainty

Electricity Cost-Sharing in Energy Communities Under Dynamic Pricing and Uncertainty

Robust Scheduling for Networked Microgrids Under Uncertainty

Chance‐constrained scheduling of hybrid microgrids under transactive energy control

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